Bibliographic Collection

Data source: Clarivate Analytics Web of Science (http://apps.webofknowledge.com)

Data format: Plaintext

Query: TO = “Capacitated Arc Routing” OR “Capacitated General Routing”

Timespan: 2010-2019

Document Type: Articles, letters, review and proceedings papers

Query data: 12 May, 2019

Install and load bibliometrix R-package

# Stable version from CRAN (Comprehensive R Archive Network)
# if you need to execute the code, remove # from the beginning of the next line

# install.packages("bibliometrix")


# Most updated version from GitHub
# if you need to execute the code, remove # from the beginning of the next lines

# install.packages("devtools")
# devtools::install_github("massimoaria/bibliometrix")

library(bibliometrix)
## Warning: package 'bibliometrix' was built under R version 3.5.2
## To cite bibliometrix in publications, please use:
## 
## Aria, M. & Cuccurullo, C. (2017) bibliometrix: An R-tool for comprehensive science mapping analysis, Journal of Informetrics, 11(4), pp 959-975, Elsevier.
##                         
## 
## http:\\www.bibliometrix.org
## 
##                         
## To start with the shiny web-interface, please digit:
## biblioshiny()

Data Loading and Converting

# Loading txt or bib files into R environment
D <- readFiles("../data/arp_grp_2010_2019_references.bib")

# Converting the loaded files into a R bibliographic dataframe
M <- convert2df(D, dbsource="isi",format="bibtex")
## 
## Converting your isi collection into a bibliographic dataframe
## 
## Articles extracted   100 
## Articles extracted   200 
## Articles extracted   237 
## Done!
## 
## 
## Generating affiliation field tag AU_UN from C1:  Done!

Section 1: Descriptive Analysis

Although bibliometrics is mainly known for quantifying the scientific production and measuring its quality and impact, it is also useful for displaying and analysing the intellectual, conceptual and social structures of research as well as their evolution and dynamical aspects.

In this way, bibliometrics aims to describe how specific disciplines, scientific domains, or research fields are structured and how they evolve over time. In other words, bibliometric methods help to map the science (so-called science mapping) and are very useful in the case of research synthesis, especially for the systematic ones.

Bibliometrics is an academic science founded on a set of statistical methods, which can be used to analyze scientific big data quantitatively and their evolution over time and discover information. Network structure is often used to model the interaction among authors, papers/documents/articles, references, keywords, etc.

Bibliometrix is an open-source software for automating the stages of data-analysis and data-visualization. After converting and uploading bibliographic data in R, Bibliometrix performs a descriptive analysis and different research-structure analysis.

Descriptive analysis provides some snapshots about the annual research development, the top “k” productive authors, papers, countries and most relevant keywords.

Main findings about the collection

#options(width=160)
results <- biblioAnalysis(M)
summary(results, k=10, pause=F, width=130)


Main Information about data

 Documents                             237 
 Sources (Journals, Books, etc.)       100 
 Keywords Plus (ID)                    207 
 Author's Keywords (DE)                421 
 Period                                2000 - 2019 
 Average citations per documents       16.09 

 Authors                               368 
 Author Appearances                    745 
 Authors of single-authored documents  11 
 Authors of multi-authored documents   357 
 Single-authored documents             12 

 Documents per Author                  0.644 
 Authors per Document                  1.55 
 Co-Authors per Documents              3.14 
 Collaboration Index                   1.59 
 
 Document types                     
 ARTICLE                         154 
 ARTICLE, BOOK CHAPTER           9 
 ARTICLE, DATA PAPER             1 
 ARTICLE, PROCEEDINGS PAPER      21 
 PROCEEDINGS PAPER               52 
 

Annual Scientific Production

 Year    Articles
    2000        3
    2001        7
    2002        2
    2003        9
    2004        6
    2005       10
    2006        8
    2007        6
    2008       15
    2009       13
    2010       19
    2011       10
    2012       10
    2013       13
    2014       28
    2015       17
    2016       24
    2017       12
    2018       17
    2019        8

Annual Percentage Growth Rate 5.297827 


Most Productive Authors

   Authors        Articles Authors        Articles Fractionalized
1      PRINS C          20     PRINS C                       7.20
2      CORBERAN A       17     CORBERAN A                    5.10
3      YAO X            17     YAO X                         5.08
4      SANCHIS JM       13     WOHLK S                       4.50
5      MEI Y            12     LAPORTE G                     4.25
6      TANG K           12     SANCHIS JM                    3.82
7      LAPORTE G        11     MEI Y                         3.75
8      LAGANA D         10     TANG K                        3.67
9      LACOMME P         9     LACOMME P                     2.83
10     BENAVENT E        8     LAGANA D                      2.58


Top manuscripts per citations

                                                         Paper           TC TCperYear
1  HERTZ A, 2000, OPER RES                                              140      7.37
2  LACOMME P, 2004, ANN OPER RES                                        131      8.73
3  BEULLENS P, 2003, EUR J OPER RES                                     111      6.94
4  TANG K, 2009, IEEE TRANS EVOL COMPUT                                  98      9.80
5  MEI Y, 2011, IEEE TRANS EVOL COMPUT                                   96     12.00
6  BRANDAO J, 2008, COMPUT OPER RES                                      88      8.00
7  LONGO H, 2006, COMPUT OPER RES                                        85      6.54
8  BELENGUER JM, 2003, COMPUT OPER RES                                   83      5.19
9  GREISTORFER P, 2003, COMPUT IND ENG                                   66      4.12
10 LACOMME P, 2001, APPLICATIONS OF EVOLUTIONARY COMPUTING, PROCEEDINGS  66      3.67


Corresponding Author's Countries

          Country Articles   Freq SCP MCP MCP_Ratio
1  CHINA                35 0.1477  18  17     0.486
2  SPAIN                31 0.1308  16  15     0.484
3  FRANCE               27 0.1139  24   3     0.111
4  ITALY                17 0.0717  10   7     0.412
5  CANADA               14 0.0591  10   4     0.286
6  PORTUGAL             13 0.0549   7   6     0.462
7  BRAZIL               11 0.0464  11   0     0.000
8  GERMANY               9 0.0380   9   0     0.000
9  UNITED KINGDOM        9 0.0380   6   3     0.333
10 AUSTRALIA             8 0.0338   4   4     0.500


SCP: Single Country Publications

MCP: Multiple Country Publications


Total Citations per Country

     Country      Total Citations Average Article Citations
1  FRANCE                     627                     23.22
2  SPAIN                      552                     17.81
3  CHINA                      520                     14.86
4  PORTUGAL                   279                     21.46
5  CANADA                     265                     18.93
6  SWITZERLAND                204                    102.00
7  ITALY                      197                     11.59
8  BRAZIL                     187                     17.00
9  BELGIUM                    151                     50.33
10 AUSTRIA                    136                     45.33


Most Relevant Sources

                                   Sources        Articles
1  COMPUTERS \\& OPERATIONS RESEARCH                    29
2  EUROPEAN JOURNAL OF OPERATIONAL RESEARCH             26
3  NETWORKS                                             13
4  JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY           7
5  OPERATIONS RESEARCH                                   7
6  ARC ROUTING: PROBLEMS METHODS AND APPLICATIONS        6
7  IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION         6
8  MATHEMATICAL PROGRAMMING                              6
9  TRANSPORTATION SCIENCE                                6
10 ANNALS OF OPERATIONS RESEARCH                         4


Most Relevant Keywords

         Author Keywords (DE)      Articles  Keywords-Plus (ID)     Articles
1  CAPACITATED ARC ROUTING PROBLEM       52 ALGORITHM                     60
2  ARC ROUTING                           36 ALGORITHMS                    31
3  HEURISTICS                            21 RURAL POSTMAN PROBLEM         28
4  VEHICLE ROUTING                       15 OPTIMIZATION                  26
5  MEMETIC ALGORITHM                     13 SEARCH                        24
6  COMBINATORIAL OPTIMIZATION            11 BOUNDS                        20
7  CAPACITATED ARC ROUTING               10 TABU SEARCH ALGORITHM         18
8  CARP                                  10 GENERAL ROUTING PROBLEM       16
9  LOCAL SEARCH                          10 INEQUALITIES                  16
10 ROUTING                               10 POLYHEDRON                    16
plot(x=results, k=10, pause=F)

Warning: Removed 1 rows containing missing values (position_stack).
Warning: Removed 1 rows containing missing values (geom_path).

Most Cited References

CR <- citations(M, field = "article", sep = ";")
cbind(CR$Cited[1:20])
                                                                                   [,1]
GOLDEN BL, 1981, NETWORKS, V11, P305, DOI 10.1002/NET.3230110308.                   144
GOLDEN BL, 1983, COMPUT OPER RES, V10, P47, DOI 10.1016/0305-0548(83)90026-6.       100
HERTZ A, 2000, OPER RES, V48, P129, DOI 10.1287/OPRE.48.1.129.12455.                 88
LACOMME P, 2004, ANN OPER RES, V131, P159, DOI 10.1023/B:ANOR.0000039517.35989.6D.   88
BEULLENS P, 2003, EUR J OPER RES, V147, P629, DOI 10.1016/S0377-2217(02)00334-X.     82
BENAVENT E, 1992, NETWORKS, V22, P669, DOI 10.1002/NET.3230220706.                   78
DROR M., 2000, ARC ROUTING THEORY S.                                                 74
BELENGUER JM, 2003, COMPUT OPER RES, V30, P705, DOI 10.1016/S0305-0548(02)00046-1.   71
ULUSOY G, 1985, EUR J OPER RES, V22, P329, DOI 10.1016/0377-2217(85)90252-8.         71
BRANDAO J, 2008, COMPUT OPER RES, V35, P1112, DOI 10.1016/J.COR.2006.07.007.         70
EGLESE RW, 1994, DISCRETE APPL MATH, V48, P231, DOI 10.1016/0166-218X(92)00003-5.    60
LONGO H, 2006, COMPUT OPER RES, V33, P1823, DOI 10.1016/J.COR.2004.11.020.           56
EISELT HA, 1995, OPER RES, V43, P399, DOI 10.1287/OPRE.43.3.399.                     48
TANG K, 2009, IEEE T EVOLUT COMPUT, V13, P1151, DOI 10.1109/TEVC.2009.2023449.       48
LI LYO, 1996, J OPER RES SOC, V47, P217, DOI 10.1057/JORS.1996.20.                   47
HERTZ A, 2001, TRANSPORT SCI, V35, P425, DOI 10.1287/TRSC.35.4.425.10431.            45
BELENGUER JM, 2006, COMPUT OPER RES, V33, P3363, DOI 10.1016/J.COR.2005.02.009.      44
LACOMME P, 2001, LECT NOTES COMPUT SC, V2037, P473.                                  42
BALDACCI R, 2006, NETWORKS, V47, P52, DOI [10.1002/NET.20091, 10.1002/NET.20091].    41
HIRABAYASHI R, 1992, ASIA PAC J OPER RES, V9, P155.                                  38

Section 2: The Intellectual Structure of the field - Co-citation Analysis

Citation analysis is one of the main classic techniques in bibliometrics. It shows the structure of a specific field through the linkages between nodes (e.g. authors, papers, journal), while the edges can be differently interpretated depending on the network type, that are namely co-citation, direct citation, bibliographic coupling. Please see Aria, Cuccurullo (2017).

Below there are three examples.

First, a co-citation network that shows relations between cited-reference works (nodes).

Second, a co-citation network that uses cited-journals as unit of analysis.

The useful dimensions to comment the co-citation networks are: (i) centrality and peripherality of nodes, (ii) their proximity and distance, (iii) strength of ties, (iv) clusters, (iiv) bridging contributions.

Third, a historiograph is built on direct citations. It draws the intellectual linkages in a historical order. Cited works of thousands of authors contained in a collection of published scientific articles is sufficient for recostructing the historiographic structure of the field, calling out the basic works in it.

Article (References) co-citation analysis

Plot options:

  • n = 50 (the funxtion plots the main 50 cited references)

  • type = “fruchterman” (the network layout is generated using the Fruchterman-Reingold Algorithm)

  • size.cex = TRUE (the size of the vertices is proportional to their degree)

  • size = 20 (the max size of vertices)

  • remove.multiple=FALSE (multiple edges are not removed)

  • labelsize = 0.7 (defines the size of vertex labels)

  • edgesize = 10 (The thickness of the edges is proportional to their strength. Edgesize defines the max value of the thickness)

  • edges.min = 5 (plots only edges with a strength greater than or equal to 5)

  • all other arguments assume the default values

NetMatrix <- biblioNetwork(M, analysis = "co-citation", network = "references", sep = ";")
net=networkPlot(NetMatrix, n = 50, Title = "Co-Citation Network", type = "fruchterman", size.cex=TRUE, size=20, remove.multiple=FALSE, labelsize=0.7,edgesize = 10, edges.min=5)

Descriptive analysis of Article co-citation network characteristics

#netstat <- networkStat(NetMatrix)
#summary(netstat,k=10)

Journal (Source) co-citation analysis

M=metaTagExtraction(M,"CR_SO",sep=";")
NetMatrix <- biblioNetwork(M, analysis = "co-citation", network = "sources", sep = ";")
net=networkPlot(NetMatrix, n = 50, Title = "Co-Citation Network", type = "auto", size.cex=TRUE, size=15, remove.multiple=FALSE, labelsize=0.7,edgesize = 10, edges.min=5)

Descriptive analysis of Journal co-citation network characteristics

netstat <- networkStat(NetMatrix)
summary(netstat,k=10)


Main statistics about the network

 Size                                  1299 
 Density                               0.041 
 Transitivity                          0.216 
 Diameter                              3 
 Degree Centralization                 0.87 
 Average path length                   1.984 
 

Section 3: Historiograph - Direct citation linkages

histResults <- histNetwork(M, min.citations=quantile(M$TC,0.75), sep = ";")
## Articles analysed   63
options(width = 130)
net <- histPlot(histResults, n=20, size.cex=TRUE, size = 5, labelsize = 3, arrowsize = 0.5)


 Legend

                                        Paper                                DOI Year LCS GCS
2000 - 1       AMBERG A, 2000, EUR J OPER RES      10.1016/S0377-2217(99)00170-8 2000  23  46
2000 - 2              HERTZ A, 2000, OPER RES        10.1287/OPRE.48.1.129.12455 2000  88 140
2001 - 4             GHIANI G, 2001, NETWORKS                      10.1002/NET.3 2001  24  41
2001 - 5       CORBERAN A, 2001, MATH PROGRAM                 10.1007/PL00011426 2001  23  32
2003 - 10    BEULLENS P, 2003, EUR J OPER RES      10.1016/S0377-2217(02)00334-X 2003  82 111
2003 - 12 BELENGUER JM, 2003, COMPUT OPER RES      10.1016/S0305-0548(02)00046-1 2003  71  83
2003 - 14 GREISTORFER P, 2003, COMPUT IND ENG      10.1016/S0360-8352(02)00178-X 2003  33  66
2004 - 15       LACOMME P, 2004, ANN OPER RES 10.1023/B:ANOR.0000039517.35989.6D 2004  88 131
2005 - 18         CHU F, 2005, EUR J OPER RES         10.1016/J.EJOR.2004.08.017 2005  27  55
2005 - 19     LACOMME P, 2005, EUR J OPER RES         10.1016/J.EJOR.2004.04.021 2005  28  58
2006 - 24 BELENGUER JM, 2006, COMPUT OPER RES          10.1016/J.COR.2005.02.009 2006  44  64
2006 - 25      WOHLK S, 2006, COMPUT OPER RES          10.1016/J.COR.2005.02.015 2006  18  20
2006 - 26    LACOMME P, 2006, COMPUT OPER RES          10.1016/J.COR.2005.02.017 2006  25  64
2006 - 27      LONGO H, 2006, COMPUT OPER RES          10.1016/J.COR.2004.11.020 2006  56  85
2006 - 28          BALDACCI R, 2006, NETWORKS                  10.1002/NET.20091 2006  41  57
2008 - 33       POLACEK M, 2008, J HEURISTICS          10.1007/S10732-007-9050-2 2008  37  60
2008 - 35    BRANDAO J, 2008, COMPUT OPER RES          10.1016/J.COR.2006.07.007 2008  70  88
2009 - 42 LETCHFORD AN, 2009, COMPUT OPER RES          10.1016/J.COR.2008.09.008 2009  22  28
2010 - 46          CORBERAN A, 2010, NETWORKS                  10.1002/NET.20347 2010  35  54
2010 - 48    GOUVEIA L, 2010, COMPUT OPER RES          10.1016/J.COR.2009.06.018 2010  19  25

Section 4: The conceptual structure - Co-Word Analysis

Co-word networks show the conceptual structure, that uncovers links between concepts through term co-occurences.

Conceptual structure is often used to understand the topics covered by scholars (so-called research front) and identify what are the most important and the most recent issues.

Dividing the whole timespan in different timeslices and comparing the conceptual structures is useful to analyze the evolution of topics over time.

Bibliometrix is able to analyze keywords, but also the terms in the articles’ titles and abstracts. It does it using network analysis or correspondance analysis (CA) or multiple correspondance analysis (MCA). CA and MCA visualise the conceptual structure in a two-dimensional plot.

Co-word Analysis through Keyword co-occurrences

Plot options:

  • normalize = “association” (the vertex similarities are normalized using association strength)

  • n = 50 (the function plots the main 50 cited references)

  • type = “fruchterman” (the network layout is generated using the Fruchterman-Reingold Algorithm)

  • size.cex = TRUE (the size of the vertices is proportional to their degree)

  • size = 20 (the max size of the vertices)

  • remove.multiple=FALSE (multiple edges are not removed)

  • labelsize = 3 (defines the max size of vertex labels)

  • label.cex = TRUE (The vertex label sizes are proportional to their degree)

  • edgesize = 10 (The thickness of the edges is proportional to their strength. Edgesize defines the max value of the thickness)

  • label.n = 30 (Labels are plotted only for the main 30 vertices)

  • edges.min = 25 (plots only edges with a strength greater than or equal to 2)

  • all other arguments assume the default values

NetMatrix <- biblioNetwork(M, analysis = "co-occurrences", network = "keywords", sep = ";")
net=networkPlot(NetMatrix, normalize="association", n = 50, Title = "Keyword Co-occurrences", type = "fruchterman", size.cex=TRUE, size=20, remove.multiple=F, edgesize = 10, labelsize=3,label.cex=TRUE,label.n=30,edges.min=2)

Descriptive analysis of keyword co-occurrences network characteristics

netstat <- networkStat(NetMatrix)
summary(netstat,k=10)


Main statistics about the network

 Size                                  210 
 Density                               0.055 
 Transitivity                          0.346 
 Diameter                              6 
 Degree Centralization                 0.29 
 Average path length                   2.553 
 

Co-word Analysis through Correspondence Analysis

CS <- conceptualStructure(M, method="CA", field="ID", minDegree=10, k.max = 8, stemming=f, labelsize=8,documents=20)

Section 5: Thematic Map

Co-word analysis draws clusters of keywords. They are considered as themes, whose density and centrality can be used in classifying themes and mapping in a two-dimensional diagram.

Thematic map is a very intuitive plot and we can analyze themes according to the quadrant in which they are placed: (1) upper-right quadrant: motor-themes; (2) lower-right quadrant: basic themes; (3) lower-left quadrant: emerging or disappearing themes; (4) upper-left quadrant: very specialized/niche themes.

Please see Cobo, M. J., L?pez-Herrera, A. G., Herrera-Viedma, E., & Herrera, F. (2011). An approach for detecting, quantifying, and visualizing the evolution of a research field: A practical application to the fuzzy sets theory field. Journal of Informetrics, 5(1), 146-166.

Map=thematicMap(M, field = "ID", n = 250, minfreq = 5,
  stemming = FALSE, size = 0.5, repel = TRUE)
plot(Map$map)

Cluster description

Clusters=Map$words[order(Map$words$Cluster,-Map$words$Occurrences),]
library(dplyr)
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
CL <- Clusters %>% group_by(.data$Cluster_Label) %>% top_n(5, .data$Occurrences)
CL
## # A tibble: 22 x 5
## # Groups:   Cluster_Label [6]
##    Occurrences Words                   Cluster Color   Cluster_Label          
##          <dbl> <chr>                     <dbl> <chr>   <chr>                  
##  1          26 optimization                  1 #E41A1C optimization           
##  2           7 design                        1 #E41A1C optimization           
##  3           9 chinese postman problem       2 #377EB8 chinese postman problem
##  4           8 memetic algorithm             2 #377EB8 chinese postman problem
##  5          31 algorithms                    3 #4DAF4A algorithms             
##  6          24 search                        3 #4DAF4A algorithms             
##  7           8 postman problem               3 #4DAF4A algorithms             
##  8           7 arc routing problem           3 #4DAF4A algorithms             
##  9           7 arc routing-problems          3 #4DAF4A algorithms             
## 10          15 time windows                  4 #FF7F00 time windows           
## # ... with 12 more rows

Section 6: The social structure - Collaboration Analysis

Collaboration networks show how authors, institutions (e.g. universities or departments) and countries relate to others in a specific field of research. For example, the first figure below is a co-author network. It discovers regular study groups, hidden groups of scholars, and pivotal authors. The second figure is called “Edu collaboration network” and uncovers relevant institutions in a specific research field and their relations.

Author collaboration network

NetMatrix <- biblioNetwork(M, analysis = "collaboration",  network = "authors", sep = ";")
net=networkPlot(NetMatrix,  n = 50, Title = "Author collaboration",type = "auto", size=10,size.cex=T,edgesize = 3,labelsize=0.6)

Descriptive analysis of author collaboration network characteristics

netstat <- networkStat(NetMatrix)
summary(netstat,k=15)


Main statistics about the network

 Size                                  368 
 Density                               0.01 
 Transitivity                          0.524 
 Diameter                              8 
 Degree Centralization                 0.047 
 Average path length                   3.296 
 

Edu collaboration network

NetMatrix <- biblioNetwork(M, analysis = "collaboration",  network = "universities", sep = ";")
net=networkPlot(NetMatrix,  n = 50, Title = "Edu collaboration",type = "auto", size=10,size.cex=T,edgesize = 3,labelsize=0.6)

Descriptive analysis of edu collaboration network characteristics

netstat <- networkStat(NetMatrix)
summary(netstat,k=15)


Main statistics about the network

 Size                                  192 
 Density                               0.011 
 Transitivity                          0.401 
 Diameter                              7 
 Degree Centralization                 0.057 
 Average path length                   3.13 
 

Country collaboration network

M <- metaTagExtraction(M, Field = "AU_CO", sep = ";")
NetMatrix <- biblioNetwork(M, analysis = "collaboration",  network = "countries", sep = ";")
net=networkPlot(NetMatrix,  n = dim(NetMatrix)[1], Title = "Country collaboration",type = "sphere", size=10,size.cex=T,edgesize = 1,labelsize=0.6, cluster="none")

Descriptive analysis of country collaboration network characteristics

netstat <- networkStat(NetMatrix)
summary(netstat,k=15)


Main statistics about the network

 Size                                  41 
 Density                               0.073 
 Transitivity                          0.278 
 Diameter                              4 
 Degree Centralization                 0.302 
 Average path length                   2.363